Chapter 7 Beta diversity
load("data/data_filtdamr_80.Rdata")
load("data/beta_filtdamr_80.Rdata")
sample_metadata <- sample_metadata_filtdamr
genome_counts_filt <- genome_counts_filtdamr
genome_metadata <- genome_metadata_filtdamr
genome_tree <- genome_tree_filtdamr
genome_gifts <- genome_gifts_filtdamr
phylum_colors <- phylum_colors_filtdamrbeta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, tree = genome_tree)
genome_counts_filt_func <- genome_counts_filt[genome_counts_filt$genome %in% rownames(genome_gifts),]
genome_counts_filt_func <- genome_counts_filt[genome_counts_filt$genome %in% rownames(genome_gifts),]
dist <- genome_gifts %>%
to.elements(., GIFT_db) %>%
traits2dist(., method = "gower")
beta_q1f <- genome_counts_filt_func %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, dist = dist)7.1 Permanova analysis
7.1.1 Strata
Richness
sample_metadata_row<- column_to_rownames(sample_metadata, "sample")
sample_metadata_row <- sample_metadata_row[labels(beta_q0n$S), ]
betadisper(beta_q0n$S, sample_metadata$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.03602 0.0180094 2.9837 999 0.062 .
Residuals 72 0.43459 0.0060359
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.714000 0.047
protected 0.724845 0.023
urban 0.044422 0.021094 adonis2(beta_q0n$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))) %>% pull(region)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 2.4713937 | 0.09990627 | 4.204275 | 0.001 |
| season | 1 | 1.0483711 | 0.04238048 | 3.566927 | 0.001 |
| type:season | 2 | 0.9372685 | 0.03788915 | 1.594459 | 0.007 |
| Residual | 69 | 20.2800898 | 0.81982411 | NA | NA |
| Total | 74 | 24.7371231 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q0n$S ~ type * season , strata = region , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 1.4635 0.08006 4.7898 0.001 ***
season 1 0.9775 0.05347 3.1991 0.001 ***
type:season 1 0.5610 0.03069 1.8360 0.007 **
Residual 50 15.2773 0.83577
Total 53 18.2792 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 1.2906 0.07930 4.3514 0.001 ***
season 1 0.6345 0.03899 2.1392 0.002 **
type:season 1 0.4088 0.02512 1.3785 0.035 *
Residual 47 13.9396 0.85658
Total 50 16.2735 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.9002 0.06603 3.2536 0.001 ***
season 1 0.9641 0.07072 3.4846 0.001 ***
type:season 1 0.4255 0.03121 1.5378 0.040 *
Residual 41 11.3433 0.83205
Total 44 13.6329 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.09096 0.045478 5.7232 999 0.006 **
Residuals 72 0.57213 0.007946
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.0120000 0.006
protected 0.0102235 0.772
urban 0.0046867 0.7844374 adonis2(beta_q1n$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))) %>% pull(region)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 2.8331097 | 0.12162688 | 5.312689 | 0.001 |
| season | 1 | 1.1483346 | 0.04929861 | 4.306748 | 0.001 |
| type:season | 2 | 0.9141128 | 0.03924334 | 1.714158 | 0.009 |
| Residual | 69 | 18.3978927 | 0.78983117 | NA | NA |
| Total | 74 | 23.2934498 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1n$S ~ type * season , strata = region , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 1.8601 0.10743 6.7297 0.001 ***
season 1 1.0323 0.05962 3.7349 0.001 ***
type:season 1 0.6017 0.03475 2.1770 0.003 **
Residual 50 13.8200 0.79819
Total 53 17.3142 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 1.4101 0.08840 4.9486 0.001 ***
season 1 0.7465 0.04680 2.6197 0.001 ***
type:season 1 0.4017 0.02518 1.4097 0.037 *
Residual 47 13.3922 0.83962
Total 50 15.9504 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.8917 0.07545 3.8147 0.001 ***
season 1 0.9941 0.08412 4.2528 0.001 ***
type:season 1 0.3486 0.02950 1.4915 0.106
Residual 41 9.5836 0.81093
Total 44 11.8179 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.05728 0.028642 2.661 999 0.067 .
Residuals 72 0.77498 0.010764
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.046000 0.115
protected 0.059462 0.949
urban 0.111009 0.951525 adonis2(beta_q1p$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))) %>% pull(region)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 0.3561228 | 0.09721296 | 4.088057 | 0.014 |
| season | 1 | 0.1880028 | 0.05132022 | 4.316297 | 0.006 |
| type:season | 2 | 0.1138033 | 0.03106555 | 1.306387 | 0.243 |
| Residual | 69 | 3.0053980 | 0.82040127 | NA | NA |
| Total | 74 | 3.6633269 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1p$S ~ type * season , strata = region , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 0.29577 0.10235 6.3336 0.016 *
season 1 0.19224 0.06652 4.1165 0.006 **
type:season 1 0.06695 0.02317 1.4337 0.219
Residual 50 2.33495 0.80797
Total 53 2.88992 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.12582 0.04567 2.3870 0.225
season 1 0.09019 0.03274 1.7110 0.151
type:season 1 0.06174 0.02241 1.1713 0.367
Residual 47 2.47750 0.89919
Total 50 2.75526 1.00000
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.09601 0.06457 3.2849 0.001 ***
season 1 0.15237 0.10248 5.2131 0.001 ***
type:season 1 0.04012 0.02698 1.3727 0.255
Residual 41 1.19834 0.80597
Total 44 1.48684 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.008931 0.0044656 1.4188 999 0.246
Residuals 72 0.226611 0.0031474
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.38300 0.117
protected 0.36301 0.395
urban 0.12598 0.40392 adonis2(beta_q1f$S ~ type,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))) %>% pull(type)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 0.02226324 | 0.05258339 | 1.998067 | 1 |
| Residual | 72 | 0.40112594 | 0.94741661 | NA | NA |
| Total | 74 | 0.42338918 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1f$S ~ type * season , strata = region , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 0.01858 0.04818 4.2067 0.001 ***
season 1 0.13428 0.34814 30.3994 0.001 ***
type:season 1 0.01199 0.03108 2.7135 0.262
Residual 50 0.22087 0.57261
Total 53 0.38572 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.014221 0.04503 3.691 0.002 **
season 1 0.073060 0.23133 18.962 0.004 **
type:season 1 0.047453 0.15025 12.316 0.007 **
Residual 47 0.181089 0.57339
Total 50 0.315824 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 -0.001311 -0.00992 -0.4856 0.863
season 1 0.014945 0.11305 5.5365 0.121
type:season 1 0.007891 0.05969 2.9233 0.192
Residual 41 0.110674 0.83717
Total 44 0.132200 1.00000
attr(,"class")
[1] "pwadstrata" "list" 7.1.2 Without strata
Richness
sample_metadata_row<- column_to_rownames(sample_metadata, "sample")
sample_metadata_row <- sample_metadata_row[labels(beta_q0n$S), ]
betadisper(beta_q0n$S, sample_metadata$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.03602 0.0180094 2.9837 999 0.063 .
Residuals 72 0.43459 0.0060359
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.721000 0.036
protected 0.724845 0.028
urban 0.044422 0.021094
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.045726 0.045726 15.566 999 0.001 ***
Residuals 73 0.214440 0.002938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
autumn spring
autumn 0.001
spring 0.00018133 adonis2(beta_q0n$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 2.4713937 | 0.09990627 | 4.204275 | 0.001 |
| season | 1 | 1.0483711 | 0.04238048 | 3.566927 | 0.001 |
| type:season | 2 | 0.9372685 | 0.03788915 | 1.594459 | 0.009 |
| Residual | 69 | 20.2800898 | 0.81982411 | NA | NA |
| Total | 74 | 24.7371231 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q0n$S ~ type * season , strata = Null , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 1.4635 0.08006 4.7898 0.001 ***
season 1 0.9775 0.05347 3.1991 0.001 ***
type:season 1 0.5610 0.03069 1.8360 0.017 *
Residual 50 15.2773 0.83577
Total 53 18.2792 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 1.2906 0.07930 4.3514 0.001 ***
season 1 0.6345 0.03899 2.1392 0.005 **
type:season 1 0.4088 0.02512 1.3785 0.100 .
Residual 47 13.9396 0.85658
Total 50 16.2735 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.9002 0.06603 3.2536 0.001 ***
season 1 0.9641 0.07072 3.4846 0.001 ***
type:season 1 0.4255 0.03121 1.5378 0.049 *
Residual 41 11.3433 0.83205
Total 44 13.6329 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.09096 0.045478 5.7232 999 0.005 **
Residuals 72 0.57213 0.007946
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.0090000 0.007
protected 0.0102235 0.790
urban 0.0046867 0.7844374
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.0133 0.0133019 2.1964 999 0.13
Residuals 73 0.4421 0.0060562
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
autumn spring
autumn 0.137
spring 0.14264 adonis2(beta_q1n$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 2.8331097 | 0.12162688 | 5.312689 | 0.001 |
| season | 1 | 1.1483346 | 0.04929861 | 4.306748 | 0.001 |
| type:season | 2 | 0.9141128 | 0.03924334 | 1.714158 | 0.010 |
| Residual | 69 | 18.3978927 | 0.78983117 | NA | NA |
| Total | 74 | 23.2934498 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1n$S ~ type * season , strata = Null , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 1.8601 0.10743 6.7297 0.001 ***
season 1 1.0323 0.05962 3.7349 0.001 ***
type:season 1 0.6017 0.03475 2.1770 0.015 *
Residual 50 13.8200 0.79819
Total 53 17.3142 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 1.4101 0.08840 4.9486 0.001 ***
season 1 0.7465 0.04680 2.6197 0.001 ***
type:season 1 0.4017 0.02518 1.4097 0.116
Residual 47 13.3922 0.83962
Total 50 15.9504 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.8917 0.07545 3.8147 0.002 **
season 1 0.9941 0.08412 4.2528 0.001 ***
type:season 1 0.3486 0.02950 1.4915 0.084 .
Residual 41 9.5836 0.81093
Total 44 11.8179 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.05728 0.028642 2.661 999 0.053 .
Residuals 72 0.77498 0.010764
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.039000 0.088
protected 0.059462 0.961
urban 0.111009 0.951525
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01038 0.010385 0.8932 999 0.385
Residuals 73 0.84878 0.011627
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
autumn spring
autumn 0.389
spring 0.34774 adonis2(beta_q1p$S ~ type*season,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 0.3561228 | 0.09721296 | 4.088057 | 0.001 |
| season | 1 | 0.1880028 | 0.05132022 | 4.316297 | 0.003 |
| type:season | 2 | 0.1138033 | 0.03106555 | 1.306387 | 0.230 |
| Residual | 69 | 3.0053980 | 0.82040127 | NA | NA |
| Total | 74 | 3.6633269 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1p$S ~ type * season , strata = Null , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 0.29577 0.10235 6.3336 0.001 ***
season 1 0.19224 0.06652 4.1165 0.004 **
type:season 1 0.06695 0.02317 1.4337 0.205
Residual 50 2.33495 0.80797
Total 53 2.88992 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.12582 0.04567 2.3870 0.048 *
season 1 0.09019 0.03274 1.7110 0.126
type:season 1 0.06174 0.02241 1.1713 0.289
Residual 47 2.47750 0.89919
Total 50 2.75526 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.09601 0.06457 3.2849 0.009 **
season 1 0.15237 0.10248 5.2131 0.001 ***
type:season 1 0.04012 0.02698 1.3727 0.248
Residual 41 1.19834 0.80597
Total 44 1.48684 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.008931 0.0044656 1.4188 999 0.214
Residuals 72 0.226611 0.0031474
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
natural protected urban
natural 0.33800 0.117
protected 0.36301 0.375
urban 0.12598 0.40392
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000085 0.00008522 0.0307 999 0.849
Residuals 73 0.202452 0.00277331
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
autumn spring
autumn 0.849
spring 0.86133 adonis2(beta_q1f$S ~ type,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| type | 2 | 0.02226324 | 0.05258339 | 1.998067 | 0.196 |
| Residual | 72 | 0.40112594 | 0.94741661 | NA | NA |
| Total | 74 | 0.42338918 | 1.00000000 | NA | NA |
$parent_call
[1] "beta_q1f$S ~ type * season , strata = Null , permutations 999"
$natural_vs_protected
Df SumOfSqs R2 F Pr(>F)
type 1 0.01858 0.04818 4.2067 0.103
season 1 0.13428 0.34814 30.3994 0.001 ***
type:season 1 0.01199 0.03108 2.7135 0.179
Residual 50 0.22087 0.57261
Total 53 0.38572 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$natural_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 0.014221 0.04503 3.691 0.123
season 1 0.073060 0.23133 18.962 0.004 **
type:season 1 0.047453 0.15025 12.316 0.012 *
Residual 47 0.181089 0.57339
Total 50 0.315824 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
$protected_vs_urban
Df SumOfSqs R2 F Pr(>F)
type 1 -0.001311 -0.00992 -0.4856 0.745
season 1 0.014945 0.11305 5.5365 0.089 .
type:season 1 0.007891 0.05969 2.9233 0.167
Residual 41 0.110674 0.83717
Total 44 0.132200 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
attr(,"class")
[1] "pwadstrata" "list" 7.2 Beta diversity plots
7.2.1 Richness
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(season) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = season, fill = season, shape = as.factor(type))) +
geom_point(size = 4) +
scale_color_manual(values = season_colors) +
#scale_shape_manual(values = 1:10)
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Type of pond")7.2.2 Neutral
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(type) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = type, shape = as.factor(season))) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=type_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Season",color='Type of pond')
#### In spring
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
filter(!region %in% c("Eskoriatza","Villabona")) %>%
# filter(season=="autumn") %>%
filter(season=="spring") %>%
group_by(type) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = type)) +#, shape = as.factor(type)
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=type_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) + labs(color='Type of pond') 
7.2.2.1 In autumn
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
filter(!region %in% c("Eskoriatza","Villabona")) %>%
filter(season=="autumn") %>%
# filter(season=="spring") %>%
group_by(type) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = type)) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=type_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(color='Type of pond')
7.2.3 Phylogenetic
beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(season) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = season, shape = as.factor(type))) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=season_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Type of pond",color='Season')beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, shape = as.factor(season))) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=location_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Season",color='Type of pond')+
geom_text(aes(label = sample), size=4)7.2.4 Functional
beta_q1f$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
# filter(!sample %in% c("EHI01629", "EHI01630", "EHI01614", "EHI01662", "EHI01446", "EHI01624")) %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(type) %>%
mutate(x_cen = median(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = median(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = type, shape = season)) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values=type_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Season",color='Type of pond')